Valutare le prestazioni della regressione

Ora che hai addestrato un modello, reg, usando tutte le feature di sales_df e hai generato le previsioni dei valori di vendita, puoi valutarne le prestazioni con alcune metriche comuni per la regressione.

Le variabili X_train, X_test, y_train, y_test e y_pred, insieme al modello addestrato reg, tutte dall'esercizio precedente, sono già state caricate per te.

Il tuo compito è capire quanto bene le feature riescono a spiegare la varianza dei valori target e valutare la capacità del modello di fare previsioni su dati non visti.

Questo esercizio fa parte del corso

Apprendimento supervisionato con scikit-learn

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Istruzioni dell'esercizio

Importa root_mean_squared_error.
Calcola l'R-squared del modello passando i valori delle feature di test e i valori target di test a un metodo appropriato.
Calcola la root mean squared error del modello usando y_test e y_pred.
Stampa r_squared e rmse.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import root_mean_squared_error
from ____.____ import ____

# Compute R-squared
r_squared = reg.____(____, ____)

# Compute RMSE
rmse = ____(____, ____)

# Print the metrics
print("R^2: {}".format(____))
print("RMSE: {}".format(____))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Apprendimento supervisionato con scikit-learn

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In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Exercise 1: Machine learning with scikit-learn Exercise 2: Binary classification Exercise 3: The supervised learning workflow Exercise 4: The classification challenge Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Predict Exercise 7: Measuring model performance Exercise 8: Train/test split + computing accuracy Exercise 9: Overfitting and underfitting Exercise 10: Visualizing model complexity

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Exercise 1: Introduzione alla regressione Exercise 2: Creazione di caratteristiche Exercise 3: Costruire un modello di regressione lineare Exercise 4: Visualizzazione di un modello di regressione lineare Exercise 5: Le basi della regressione lineare Exercise 6: Adattamento e previsione per la regressione Exercise 7: Valutare le prestazioni della regressione

Esercizio in corso

Exercise 8: Convalida incrociata Exercise 9: Convalida incrociata per l'R-squared Exercise 10: Analisi delle metriche di convalida incrociata Exercise 11: Regressione regolarizzata Exercise 12: Regressione regolarizzata: Crinale Exercise 13: Regressione Lasso per l'importanza delle caratteristiche

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

Exercise 1: How good is your model?Exercise 2: Deciding on a primary metric Exercise 3: Assessing a diabetes prediction classifier Exercise 4: Logistic regression and the ROC curve Exercise 5: Building a logistic regression model Exercise 6: The ROC curve Exercise 7: ROC AUC Exercise 8: Hyperparameter tuning Exercise 9: Hyperparameter tuning with GridSearchCV Exercise 10: Hyperparameter tuning with RandomizedSearchCV

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Exercise 1: Preprocessing data Exercise 2: Creating dummy variables Exercise 3: Regression with categorical features Exercise 4: Handling missing data Exercise 5: Dropping missing data Exercise 6: Pipeline for song genre prediction: I Exercise 7: Pipeline for song genre prediction: II Exercise 8: Centering and scaling Exercise 9: Centering and scaling for regression Exercise 10: Centering and scaling for classification Exercise 11: Evaluating multiple models Exercise 12: Visualizing regression model performance Exercise 13: Predicting on the test set Exercise 14: Visualizing classification model performance Exercise 15: Pipeline for predicting song popularity Exercise 16: Congratulations